import numpy as np
import pandas as pd
import plotly.graph_objects as go
from nsepython import *
from datetime import date, timedelta

import warnings
warnings.filterwarnings('ignore')

# I've already downloaded data .csv file from NSE website
top20_nifty_stocks = pd.read_csv('T20-GL-gainers-NIFTY-26-Jul-2024.csv')
top20_nifty_stocks

top20_stocks = top20_nifty_stocks['Symbol'].values
top20_stocks

array(['SHRIRAMFIN', 'CIPLA', 'DIVISLAB', 'BHARTIARTL', 'APOLLOHOSP',
       'ADANIPORTS', 'WIPRO', 'TATASTEEL', 'ADANIENT', 'SUNPHARMA',
       'SBILIFE', 'LTIM', 'HCLTECH', 'HINDALCO', 'INFY', 'HDFCLIFE',
       'COALINDIA', 'JSWSTEEL', 'M&M', 'ITC'], dtype=object)

# Define the start and end dates for the 5 year period
end_date = date.today()
start_date = end_date - timedelta(days=5*365)
end_date = end_date.strftime('%d-%m-%Y')
start_date = start_date.strftime('%d-%m-%Y')
print('start_date:', start_date)
print('end_date:', end_date)

start_date: 29-07-2019
end_date: 27-07-2024

def get_stock_data(stock_name: str, start_date=start_date, end_date=end_date) -> pd.DataFrame:
    """
    Retrieves historical stock data for the specified stock within the given date range(Default Date Range - 5 Years from today).

    Args:
        stock_name (str): The symbol of the stock for which to retrieve data.
        start_date (str, optional): The starting date of the date range in the format 'DD-MM-YYYY'. Defaults to the global `start_date` variable.
        end_date (str, optional): The ending date of the date range in the format 'DD-MM-YYYY'. Defaults to the global `end_date` variable.

    Returns:
        pd.DataFrame: A DataFrame containing the historical stock data with the following columns:
            - Symbol: The stock symbol
            - Date: The stock date records
            - Year: The particular year
            - High: The daily high price
            - Low: The daily low price
            - Open: The daily opening price
            - Close: The daily closing price
            - Volume: The daily trading volume
    """
    # Generating Stock Data with `nsepython` library by providing symbol, start date & end date
    # Values sorted ascending by 'CH_TIMESTAMP' column (i.e. by Date: start to end)
    stock_data_raw = equity_history(stock_name, 'EQ', start_date, end_date)
    # Selecting Important Column for our Analysis
    important_cols = ['CH_SYMBOL', 'CH_TIMESTAMP', 'CH_TRADE_HIGH_PRICE', 'CH_TRADE_LOW_PRICE', 'CH_OPENING_PRICE', 'CH_CLOSING_PRICE', 'CH_TOT_TRADED_QTY']
    # Sub-setting Stock Data with important columns only
    stock_data = stock_data_raw[important_cols]
    # Renaming columns
    stock_data = stock_data.rename(columns={
        'CH_SYMBOL': 'Symbol',
        'CH_TIMESTAMP': 'Date',
        'CH_TRADE_HIGH_PRICE': 'High',
        'CH_TRADE_LOW_PRICE': 'Low',
        'CH_OPENING_PRICE': 'Open',
        'CH_CLOSING_PRICE': 'Close',
        'CH_TOT_TRADED_QTY': 'Volume'
    })
    stock_data['Date'] = pd.to_datetime(stock_data['Date'])
    stock_data['Year'] = stock_data['Date'].dt.year
    stock_data = stock_data[['Symbol','Date','Year','Low','High','Open','Close','Volume']].sort_values(by='Date')
    return stock_data

# Taking only the top stock for now; symbol named "SHRIRAMFIN"
SHRIRAMFIN = get_stock_data('SHRIRAMFIN')
SHRIRAMFIN

def simple_moving_average_crossover(df):
    """
    Implement a simple moving average crossover strategy.

    Parameters:
    df (pandas DataFrame): The historical data for a stock.

    Returns:
    pandas DataFrame: The backtested data with additional columns for strategy signals.
    """
    # Calculate the 50-day and 200-day simple moving averages
    df['50d'] = df['Close'].rolling(window=50).mean()
    df['200d'] = df['Close'].rolling(window=200).mean()

    # Generate buy/sell signals
    df['Signal'] = 0.0
    # Signal Value 1 depicts Buy Signal; 0 depicts Sell Signal
    df['Signal'] = df['50d'] > df['200d']  # True if 50d > 200d, else False
    df['Signal'] = df['Signal'].astype(int)  # Convert boolean to int (1 for buy, 0 for sell)
    
    # Buy Signal: When 50SMA line cross above 200SMA line, indicating Bull Market Sign
    # Bear Signal: When 50SMA line cross below 200SMA line, indicating Bear Market Sign

    # Create a column for entry/exit signals
    df['Entry/Exit'] = df['Signal'].diff()

    # Calculate strategy returns based on entry signals
    df['Strategy Returns'] = df['Close'].pct_change() * df['Signal'].shift(1)  # Use previous signal for returns
    df['Cumulative Strategy Returns'] = (1 + df['Strategy Returns']).cumprod() - 1  # Cumulative returns

    return df

simple_moving_average_crossover(SHRIRAMFIN)

def calculate_performance_metrics(df):
    """
    Calculate key performance metrics for the backtested data.

    Parameters:
    df (pandas DataFrame): The backtested data.

    Returns:
    pandas DataFrame: The backtested data with additional columns for performance metrics.
    """
    # Calculate total returns
    df['Total Returns'] = df['Strategy Returns'].cumprod() - 1

    # Calculate annualized returns
    # Assuming daily returns, we can calculate annualized returns as follows:
    trading_days = 252  # Typical number of trading days in a year
    df['Annualized Returns'] = (1 + df['Total Returns']) ** (trading_days / len(df)) - 1

    # Calculate maximum drawdown
    df['High Watermark'] = df['Strategy Returns'].cummax()
    df['Drawdown'] = df['High Watermark'] - df['Strategy Returns']
    df['Max Drawdown'] = df['Drawdown'].max()

    # Calculate Sharpe ratio
    risk_free_rate = 0.0  # Assuming a risk-free rate of 0 for simplicity
    df['Sharpe Ratio'] = (df['Annualized Returns'] - risk_free_rate) / df['Strategy Returns'].std() * np.sqrt(trading_days)

    # Calculate win/loss ratio
    wins = df['Strategy Returns'][df['Strategy Returns'] > 0].count()
    losses = df['Strategy Returns'][df['Strategy Returns'] <= 0].count()
    df['Win/Loss Ratio'] = wins / losses if losses > 0 else wins

    # Calculate number of trades executed
    df['Number of Trades'] = df['Signal'].diff().ne(0).sum()
    
    return df

# Apply the performance metrics calculation
calculate_performance_metrics(SHRIRAMFIN)

def plot_backtested_data(backtested_data: pd.DataFrame, stock_name: str):
    df = backtested_data

    # Create the tooltip text with labels for candlestick
    df['tooltip'] = (
            'Date: ' + df['Date'].dt.strftime('%Y-%m-%d') + '<br>' +
            'Open: ' + df['Open'].astype(str) + '<br>' +
            'Low: ' + df['Low'].astype(str) + '<br>' +
            'Close: ' + df['Close'].astype(str) + '<br>' +
            'High: ' + df['High'].astype(str) + '<br>' +
            'Volume: ' + df['Volume'].astype(str)
    )

    # Create the candlestick trace
    candlestick = go.Candlestick(
        x=df['Date'],  # Change x-axis to df['Date']
        open=df['Open'],
        high=df['High'],
        low=df['Low'],
        close=df['Close'],
        name='Candlestick',
        hovertext=df['tooltip'],  # Use the custom tooltip
        hoverinfo='text'  # Show custom tooltip
    )

    # Create the 50-day SMA trace
    sma50 = go.Scatter(
        x=df['Date'],  # Change x-axis to df['Date']
        y=df['50d'],
        mode='lines',
        name='50-day SMA',
        line=dict(color='darkorchid')
    )

    # Create the 200-day SMA trace
    sma200 = go.Scatter(
        x=df['Date'],  # Change x-axis to df['Date']
        y=df['200d'],
        mode='lines',
        name='200-day SMA',
        line=dict(color='violet')
    )

    # Create the buy signal trace with tooltip
    buy_signals = df[df['Entry/Exit'] == 1]
    buy_trace = go.Scatter(
        x=buy_signals['Date'],  # Change x-axis to df['Date']
        y=buy_signals['Close'],
        mode='markers',
        marker=dict(
            symbol='triangle-up',
            size=25,
            color='green'
        ),
        name='Buy Signal',
        hovertext=(
                'Date: ' + buy_signals['Date'].dt.strftime('%Y-%m-%d') + '<br>' +
                'Close: ' + buy_signals['Close'].astype(str) + '<br>' +
                'Signal: Buy'
        ),
        hoverinfo='text'  # Show custom tooltip
    )

    # Create the sell signal trace with tooltip
    sell_signals = df[df['Entry/Exit'] == -1]
    sell_trace = go.Scatter(
        x=sell_signals['Date'],  # Change x-axis to df['Date']
        y=sell_signals['Close'],
        mode='markers',
        marker=dict(
            symbol='triangle-down',
            size=25,
            color='red'
        ),
        name='Sell Signal',
        hovertext=(
                'Date: ' + sell_signals['Date'].dt.strftime('%Y-%m-%d') + '<br>' +
                'Close: ' + sell_signals['Close'].astype(str) + '<br>' +
                'Signal: Sell'
        ),
        hoverinfo='text'  # Show custom tooltip
    )

    # Create the layout with quarterly ticks
    layout = go.Layout(
        title=f"{stock_name} - Simple Moving Average Crossover Strategy",
        xaxis_title="Date",
        yaxis_title="Price",
        xaxis_rangeslider_visible=False,
        width=1700,
        height=900,
        xaxis=dict(
            constrain='domain',
            range=[df['Date'].min() - pd.Timedelta(days=15), df['Date'].max() + pd.Timedelta(days=15)],  # Add padding
            tickvals=pd.date_range(start=df['Date'].min(), end=df['Date'].max(), freq='Q'),  # Quarterly ticks
            ticktext=pd.date_range(start=df['Date'].min(), end=df['Date'].max(), freq='Q').strftime('%Y-%m')  # Format ticks as Year-Month
        )
    )
    
    # Create the figure and plot
    fig = go.Figure(data=[candlestick, sma50, sma200, buy_trace, sell_trace], layout=layout)
    fig.update_layout(template='plotly_white')
    fig.show()

plot_backtested_data(SHRIRAMFIN, 'SHRIRAMFIN')

# Breakdown of performance metrics on a yearly basis
def get_yearly_performance_metrics(backtested_data):
    yearly_performance = backtested_data.groupby('Year').agg({
        'Total Returns': ['mean', 'std'],
        'Annualized Returns': ['mean', 'std'],
        'Max Drawdown': ['mean', 'std'],
        'Sharpe Ratio': ['mean', 'std'],
        'Win/Loss Ratio': ['mean', 'std'],
        'Number of Trades': ['mean', 'std']
    })
    
    # Flatten the multi-level column index
    yearly_performance.columns = ['_'.join(col).strip() for col in yearly_performance.columns.values]
    
    # Reset the index to convert the aggregated data to a DataFrame
    yearly_performance = yearly_performance.reset_index()
    
    # Rename the columns for clarity
    yearly_performance.columns = ['Year'] + [col for col in yearly_performance.columns if col != 'Year']
    
    # Returning Dataframe
    return yearly_performance

# Outputting performance metrics on a yearly basis
get_yearly_performance_metrics(SHRIRAMFIN)

data = get_stock_data('SHRIRAMFIN')
data

from backtesting import Backtest, Strategy
from backtesting.lib import crossover
from backtesting.test import SMA
import pandas as pd

class SMACross(Strategy):
    def init(self):
        # Define two moving averages
        self.sma50 = self.I(SMA, self.data.Close, 50)  # Short-term MA
        self.sma200 = self.I(SMA, self.data.Close, 200)  # Long-term MA

    def next(self):
        # Buy when the short-term MA crosses above the long-term MA
        if crossover(self.sma50, self.sma200):
            self.buy()
        # Sell when the short-term MA crosses below the long-term MA
        elif crossover(self.sma200, self.sma50):
            self.sell()

backtest = Backtest(data, SMACross, commission=.002, exclusive_orders=True)
stats = backtest.run()
print(stats)

Start                                     0.0
End                                    1283.0
Duration                               1283.0
Exposure Time [%]                   61.137072
Equity Final [$]                    3065.8235
Equity Peak [$]                     10123.324
Return [%]                         -69.341765
Buy & Hold Return [%]              -64.781054
Return (Ann.) [%]                         0.0
Volatility (Ann.) [%]                     NaN
Sharpe Ratio                              NaN
Sortino Ratio                             NaN
Calmar Ratio                              0.0
Max. Drawdown [%]                  -79.137332
Avg. Drawdown [%]                  -79.137332
Max. Drawdown Duration                  784.0
Avg. Drawdown Duration                  784.0
# Trades                                  6.0
Win Rate [%]                              0.0
Best Trade [%]                      -0.350177
Worst Trade [%]                    -43.687348
Avg. Trade [%]                     -20.744538
Max. Trade Duration                     235.0
Avg. Trade Duration                130.666667
Profit Factor                             0.0
Expectancy [%]                     -19.261707
SQN                                 -2.094035
_strategy                            SMACross
_equity_curve                       Equity...
_trades                      Size  EntryBa...
dtype: object

backtest.plot()

	Symbol	Open	High	Low	Prev. Close	LTP	%chng	Volume	Value	CA
0	SHRIRAMFIN	2705.80	3044.00	2690.00	2679.00	2934.00	9.52	6439265	1.850831e+10	23-Jul-2024
1	CIPLA	1504.75	1600.00	1501.00	1500.05	1586.50	5.76	4686806	7.282687e+09	02-Aug-2024
2	DIVISLAB	4570.95	4810.00	4563.85	4547.00	4792.25	5.39	1262056	5.979785e+09	02-Aug-2024
3	BHARTIARTL	1450.25	1520.00	1449.15	1449.15	1511.75	4.32	9563621	1.433788e+10	11-Aug-2023
4	APOLLOHOSP	6433.00	6679.95	6393.55	6385.80	6650.00	4.14	432670	2.851676e+09	20-Feb-2024
5	ADANIPORTS	1495.90	1547.90	1490.00	1487.00	1540.75	3.61	4500404	6.888228e+09	14-Jun-2024
6	WIPRO	510.00	528.55	508.50	506.85	524.50	3.48	13074509	6.808681e+09	16-Jul-2024
7	TATASTEEL	158.75	162.95	158.21	157.39	162.62	3.32	45636115	7.387574e+09	21-Jun-2024
8	ADANIENT	2995.00	3109.00	2988.10	2973.50	3072.25	3.32	1939775	5.948320e+09	14-Jun-2024
9	SUNPHARMA	1670.45	1723.80	1665.00	1665.80	1721.00	3.31	4591025	7.803090e+09	12-Jul-2024
10	SBILIFE	1686.25	1777.70	1686.25	1695.40	1750.60	3.26	2982225	5.216359e+09	15-Mar-2024
11	LTIM	5599.85	5811.90	5585.05	5597.90	5780.00	3.25	497156	2.861366e+09	19-Jun-2024
12	HCLTECH	1602.80	1640.00	1594.00	1587.60	1638.80	3.22	2813950	4.572641e+09	23-Jul-2024
13	HINDALCO	660.00	668.50	655.00	646.55	667.05	3.17	4730509	3.137510e+09	14-Aug-2023
14	INFY	1825.35	1883.00	1825.35	1824.85	1880.00	3.02	11368877	2.128117e+10	31-May-2024
15	HDFCLIFE	683.15	709.80	682.05	683.15	703.25	2.94	8932963	6.259774e+09	21-Jun-2024
16	COALINDIA	498.25	511.70	498.25	494.00	508.45	2.93	20437719	1.038359e+10	20-Feb-2024
17	JSWSTEEL	880.00	908.80	875.30	874.50	899.00	2.80	2056321	1.848900e+09	09-Jul-2024
18	M&M	2821.60	2897.40	2790.00	2811.40	2881.80	2.50	2352247	6.729379e+09	05-Jul-2024
19	ITC	494.05	506.40	487.40	489.95	501.55	2.37	20735172	1.033047e+10	04-Jun-2024

	Symbol	Date	Year	Low	High	Open	Close	Volume
1257	SRTRANSFIN	2019-07-29	2019	962.55	990.00	980.80	967.45	1417626
1258	SRTRANSFIN	2019-07-30	2019	964.10	982.40	967.50	969.00	1468107
1259	SRTRANSFIN	2019-07-31	2019	959.00	975.45	967.85	969.25	1528948
1260	SRTRANSFIN	2019-08-01	2019	963.30	984.70	969.00	967.35	1274941
1261	SRTRANSFIN	2019-08-02	2019	960.00	981.90	971.90	978.45	1315470
...	...	...	...	...	...	...	...	...
13	SHRIRAMFIN	2024-07-22	2024	2764.05	2847.95	2799.90	2827.40	543557
14	SHRIRAMFIN	2024-07-23	2024	2663.40	2827.00	2827.00	2739.20	2445493
15	SHRIRAMFIN	2024-07-24	2024	2665.30	2760.00	2739.00	2724.25	1026115
16	SHRIRAMFIN	2024-07-25	2024	2635.50	2710.00	2699.50	2679.00	2089082
17	SHRIRAMFIN	2024-07-26	2024	2690.00	3044.00	2705.80	2925.00	6439336

	Symbol	Date	Year	Low	High	Open	Close	Volume	50d	200d	Signal	Entry/Exit	Strategy Returns	Cumulative Strategy Returns
1257	SRTRANSFIN	2019-07-29	2019	962.55	990.00	980.80	967.45	1417626	NaN	NaN	0	NaN	NaN	NaN
1258	SRTRANSFIN	2019-07-30	2019	964.10	982.40	967.50	969.00	1468107	NaN	NaN	0	0.0	0.000000	0.000000
1259	SRTRANSFIN	2019-07-31	2019	959.00	975.45	967.85	969.25	1528948	NaN	NaN	0	0.0	0.000000	0.000000
1260	SRTRANSFIN	2019-08-01	2019	963.30	984.70	969.00	967.35	1274941	NaN	NaN	0	0.0	-0.000000	0.000000
1261	SRTRANSFIN	2019-08-02	2019	960.00	981.90	971.90	978.45	1315470	NaN	NaN	0	0.0	0.000000	0.000000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13	SHRIRAMFIN	2024-07-22	2024	2764.05	2847.95	2799.90	2827.40	543557	2636.055	2325.20975	1	0.0	0.005888	0.815245
14	SHRIRAMFIN	2024-07-23	2024	2663.40	2827.00	2827.00	2739.20	2445493	2644.232	2329.80125	1	0.0	-0.031195	0.758619
15	SHRIRAMFIN	2024-07-24	2024	2665.30	2760.00	2739.00	2724.25	1026115	2652.317	2333.95825	1	0.0	-0.005458	0.749020
16	SHRIRAMFIN	2024-07-25	2024	2635.50	2710.00	2699.50	2679.00	2089082	2659.123	2337.86025	1	0.0	-0.016610	0.719969
17	SHRIRAMFIN	2024-07-26	2024	2690.00	3044.00	2705.80	2925.00	6439336	2670.191	2343.09475	1	0.0	0.091825	0.877906

	Symbol	Date	Year	Low	High	Open	Close	Volume	50d	200d	...	Strategy Returns	Cumulative Strategy Returns	Total Returns	Annualized Returns	High Watermark	Drawdown	Max Drawdown	Sharpe Ratio	Win/Loss Ratio	Number of Trades
1257	SRTRANSFIN	2019-07-29	2019	962.55	990.00	980.80	967.45	1417626	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	0.27702	NaN	0.394565	8
1258	SRTRANSFIN	2019-07-30	2019	964.10	982.40	967.50	969.00	1468107	NaN	NaN	...	0.000000	0.000000	-1.0	-1.0	0.000000	0.000000	0.27702	-908.245604	0.394565	8
1259	SRTRANSFIN	2019-07-31	2019	959.00	975.45	967.85	969.25	1528948	NaN	NaN	...	0.000000	0.000000	-1.0	-1.0	0.000000	0.000000	0.27702	-908.245604	0.394565	8
1260	SRTRANSFIN	2019-08-01	2019	963.30	984.70	969.00	967.35	1274941	NaN	NaN	...	-0.000000	0.000000	-1.0	-1.0	-0.000000	0.000000	0.27702	-908.245604	0.394565	8
1261	SRTRANSFIN	2019-08-02	2019	960.00	981.90	971.90	978.45	1315470	NaN	NaN	...	0.000000	0.000000	-1.0	-1.0	0.000000	0.000000	0.27702	-908.245604	0.394565	8
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13	SHRIRAMFIN	2024-07-22	2024	2764.05	2847.95	2799.90	2827.40	543557	2636.055	2325.20975	...	0.005888	0.815245	-1.0	-1.0	0.162227	0.156339	0.27702	-908.245604	0.394565	8
14	SHRIRAMFIN	2024-07-23	2024	2663.40	2827.00	2827.00	2739.20	2445493	2644.232	2329.80125	...	-0.031195	0.758619	-1.0	-1.0	0.162227	0.193421	0.27702	-908.245604	0.394565	8
15	SHRIRAMFIN	2024-07-24	2024	2665.30	2760.00	2739.00	2724.25	1026115	2652.317	2333.95825	...	-0.005458	0.749020	-1.0	-1.0	0.162227	0.167684	0.27702	-908.245604	0.394565	8
16	SHRIRAMFIN	2024-07-25	2024	2635.50	2710.00	2699.50	2679.00	2089082	2659.123	2337.86025	...	-0.016610	0.719969	-1.0	-1.0	0.162227	0.178837	0.27702	-908.245604	0.394565	8
17	SHRIRAMFIN	2024-07-26	2024	2690.00	3044.00	2705.80	2925.00	6439336	2670.191	2343.09475	...	0.091825	0.877906	-1.0	-1.0	0.162227	0.070401	0.27702	-908.245604	0.394565	8

	Year	Total Returns_mean	Annualized Returns_mean	Max Drawdown_mean	Sharpe Ratio_mean	Win/Loss Ratio_mean	Number of Trades_mean
0	2019	-1.0	-1.0	0.27702	-908.245604	0.394565	8.0
1	2020	-1.0	-1.0	0.27702	-908.245604	0.394565	8.0
2	2021	-1.0	-1.0	0.27702	-908.245604	0.394565	8.0
3	2022	-1.0	-1.0	0.27702	-908.245604	0.394565	8.0
4	2023	-1.0	-1.0	0.27702	-908.245604	0.394565	8.0
5	2024	-1.0	-1.0	0.27702	-908.245604	0.394565	8.0

DATA BACKTESTING ANALYSIS PROJECT¶

(For Top 20 Stocks Listed on NSE India)¶

Author - Shubham Jangra¶

A Brief NOTE Before Starting This Project!!!¶

NSE India has discontinued old website which is used by NSEPY, check this link for reference:¶

Now we have new updated library `nsepython` instead of old `nsepy` library¶

I tried using old library but It didn't work, it wasn't fetching any data¶

I'm using the new updated library; install it with below command:¶

Importing Libraries¶

Filtering Warnings¶

Getting NSE Top 20 Gainers from official website. Click here to redirect:¶

List of all Top 20 Gainers¶

Defining Starting Date(i.e. 5 years back from today) & Ending Date(i.e. today)¶

PRIOR NOTE:¶

Why I'm doing it:¶

So, What's the Solution?¶

1. Data Acquisition and Preparation¶

2. Implementing a Simple Moving Average(SMA) Crossover Strategy¶

3. Calculating Performance Metrics:¶

4. Analysis and Insights:¶

Visualize Backtesting Results:¶

Breakdown of Performance Metrics:¶

SHRIRAMFIN - Simple Moving Average Crossover Strategy Analysis¶

This chart depicts the price action of SHRIRAMFIN along with two simple moving averages (SMAs): a 50-day SMA and a 200-day SMA. This is a common technical analysis tool for identifying potential buy and sell signals.¶

Observations:¶

Analysis:¶

Conclusion:¶

Easy BackTesting Analysis using `backtesting` python library¶

Getting Same Stock Data¶

Making a class for Simple Moving Average Cross Strategy¶

Printing Key Metrics(Stats)¶

Visualizing BackTesting Strategy¶

Creating Streamlit App¶

Find the code in `stock-analysis-streamlit-app.py`¶

END OF CODE¶

DATA BACKTESTING ANALYSIS PROJECT¶

(For Top 20 Stocks Listed on NSE India)¶

Author - Shubham Jangra¶

A Brief NOTE Before Starting This Project!!!¶

NSE India has discontinued old website which is used by NSEPY, check this link for reference:¶

Now we have new updated library nsepython instead of old nsepy library¶

I tried using old library but It didn't work, it wasn't fetching any data¶

I'm using the new updated library; install it with below command:¶

Importing Libraries¶

Filtering Warnings¶

Getting NSE Top 20 Gainers from official website. Click here to redirect:¶

List of all Top 20 Gainers¶

Defining Starting Date(i.e. 5 years back from today) & Ending Date(i.e. today)¶

PRIOR NOTE:¶

Why I'm doing it:¶

So, What's the Solution?¶

1. Data Acquisition and Preparation¶

2. Implementing a Simple Moving Average(SMA) Crossover Strategy¶

3. Calculating Performance Metrics:¶

4. Analysis and Insights:¶

Visualize Backtesting Results:¶

Breakdown of Performance Metrics:¶

SHRIRAMFIN - Simple Moving Average Crossover Strategy Analysis¶

This chart depicts the price action of SHRIRAMFIN along with two simple moving averages (SMAs): a 50-day SMA and a 200-day SMA. This is a common technical analysis tool for identifying potential buy and sell signals.¶

Observations:¶

Analysis:¶

Conclusion:¶

Easy BackTesting Analysis using backtesting python library¶

Getting Same Stock Data¶

Making a class for Simple Moving Average Cross Strategy¶

Printing Key Metrics(Stats)¶

Visualizing BackTesting Strategy¶

Creating Streamlit App¶

Find the code in stock-analysis-streamlit-app.py¶

END OF CODE¶

Now we have new updated library `nsepython` instead of old `nsepy` library¶

Easy BackTesting Analysis using `backtesting` python library¶

Find the code in `stock-analysis-streamlit-app.py`¶